[My github link] https://github.com/sydneydlu98/DSI_Data_Challenge_4
## load all the libraries
library(dplyr)
library(ggplot2)
library(plotly)
library(shiny)
library(rsconnect)
library(readr)
library(medicaldata)
View(strep_tb)
# clean data with improved patients
strep_imp <- strep_tb %>%
filter(improved == "TRUE") %>%
select(-dose_PAS_g)
# first plot - table
fig1 <- plot_ly(
type = 'table',
header = list(
values = c("<b>Control/Case</b>", "<b>radiologic_6m</b>"),
align = c("center", "center"),
line = list(width = 1, color = 'black'),
fill = list(color = c("grey", "grey")),
font = list(
family = "Arial",
size = 14,
color = "white"
)
),
cells = list(
values = rbind(t(as.matrix(
unname(strep_imp$arm)
)),
c(as.matrix(
unname(strep_imp$radiologic_6m)
))),
align = c("center", "center"),
line = list(color = "black", width = 1),
font = list(
family = "Arial",
size = 12,
color = c("black")
)
)
)
fig1
# second plot - bar chart
p2 <- ggplot(data = strep_imp,
aes(x = radiologic_6m, fill = arm)) +
geom_bar()
fig2 <- ggplotly(p2)
fig2
# third plot - scatter plot
p3 <- ggplot(data = strep_imp,
aes(x = baseline_condition, fill = arm)) +
geom_bar()
fig3 <- ggplotly(p3)
fig3